
'''simple docstring'''






import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 			UpperCAmelCase__    (							A    ,						unittest.TestCase			):
    lowerCAmelCase_					     =		LEDTokenizer
    lowerCAmelCase_					     =		LEDTokenizerFast
    lowerCAmelCase_					     =		True



    def 	lowerCamelCase_	(						self   :							Dict  ):
          super().setUp()
          _lowerCamelCase					: Tuple		    = [
              "l",
              "o",
              "w",
              "e",
              "r",
              "s",
              "t",
              "i",
              "d",
              "n",
              "\u0120",
              "\u0120l",
              "\u0120n",
              "\u0120lo",
              "\u0120low",
              "er",
              "\u0120lowest",
              "\u0120newer",
              "\u0120wider",
              "<unk>",
          ]
          _lowerCamelCase					: Union[str, Any]		    = dict(zip(__A,range(len(__A  )  )  )  )
          _lowerCamelCase					: List[str]		    = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
          _lowerCamelCase					: Dict		    = {"unk_token": "<unk>"}

          _lowerCamelCase					: Any		    = os.path.join(self.tmpdirname,VOCAB_FILES_NAMES["vocab_file"]  )
          _lowerCamelCase					: int		    = os.path.join(self.tmpdirname,VOCAB_FILES_NAMES["merges_file"]  )
          with open(self.vocab_file,"w",encoding="utf-8"  ) as fp:
                fp.write(json.dumps(__A  ) + "\n"  )
          with open(self.merges_file,"w",encoding="utf-8"  ) as fp:
                fp.write("\n".join(__A  )  )



    def 	lowerCamelCase_	(						self   :							str,**__A   :							str  ):
          kwargs.update(self.special_tokens_map  )
          return self.tokenizer_class.from_pretrained(self.tmpdirname,**__A  )



    def 	lowerCamelCase_	(						self   :							Dict,**__A   :							Dict  ):
          kwargs.update(self.special_tokens_map  )
          return self.rust_tokenizer_class.from_pretrained(self.tmpdirname,**__A  )



    def 	lowerCamelCase_	(						self   :							Any,__A   :							str  ):
          return "lower newer", "lower newer"



    @cached_property
    def 	lowerCamelCase_	(						self   :							Optional[int]  ):
          return LEDTokenizer.from_pretrained("allenai/led-base-16384"  )



    @cached_property
    def 	lowerCamelCase_	(						self   :							Optional[int]  ):
          return LEDTokenizerFast.from_pretrained("allenai/led-base-16384"  )



    @require_torch
    def 	lowerCamelCase_	(						self   :							List[Any]  ):
          _lowerCamelCase					: Optional[Any]		    = ["A long paragraph for summarization.", "Another paragraph for summarization."]
          _lowerCamelCase					: List[str]		    = [0, 2_5_0, 2_5_1, 1_7_8_1_8, 1_3, 3_9_1_8_6, 1_9_3_8, 4, 2]

          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                _lowerCamelCase					: Any		    = tokenizer(__A,max_length=len(__A  ),padding=__A,return_tensors="pt"  )
                self.assertIsInstance(__A,__A  )

                self.assertEqual((2, 9),batch.input_ids.shape  )
                self.assertEqual((2, 9),batch.attention_mask.shape  )
                _lowerCamelCase					: Dict		    = batch.input_ids.tolist()[0]
                self.assertListEqual(__A,__A  )



    @require_torch
    def 	lowerCamelCase_	(						self   :							Any  ):
          _lowerCamelCase					: Dict		    = ["A long paragraph for summarization.", "Another paragraph for summarization."]
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                _lowerCamelCase					: Optional[int]		    = tokenizer(__A,padding=__A,return_tensors="pt"  )
                self.assertIn("input_ids",__A  )
                self.assertIn("attention_mask",__A  )
                self.assertNotIn("labels",__A  )
                self.assertNotIn("decoder_attention_mask",__A  )



    @require_torch
    def 	lowerCamelCase_	(						self   :							str  ):
          _lowerCamelCase					: Dict		    = [
              "Summary of the text.",
              "Another summary.",
          ]
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                _lowerCamelCase					: Optional[int]		    = tokenizer(text_target=__A,max_length=3_2,padding="max_length",return_tensors="pt"  )
                self.assertEqual(3_2,targets["input_ids"].shape[1]  )



    @require_torch
    def 	lowerCamelCase_	(						self   :							Union[str, Any]  ):
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                _lowerCamelCase					: Tuple		    = tokenizer(
                    ["I am a small frog" * 1_0_2_4, "I am a small frog"],padding=__A,truncation=__A,return_tensors="pt"  )
                self.assertIsInstance(__A,__A  )
                self.assertEqual(batch.input_ids.shape,(2, 5_1_2_2)  )



    @require_torch
    def 	lowerCamelCase_	(						self   :							Union[str, Any]  ):
          _lowerCamelCase					: Optional[Any]		    = ["A long paragraph for summarization."]
          _lowerCamelCase					: Any		    = [
              "Summary of the text.",
          ]
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                _lowerCamelCase					: int		    = tokenizer(__A,return_tensors="pt"  )
                _lowerCamelCase					: str		    = tokenizer(text_target=__A,return_tensors="pt"  )
                _lowerCamelCase					: Union[str, Any]		    = inputs["input_ids"]
                _lowerCamelCase					: List[Any]		    = targets["input_ids"]
                self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()  )
                self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()  )
                self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()  )
                self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()  )



    @require_torch
    def 	lowerCamelCase_	(						self   :							Union[str, Any]  ):
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                _lowerCamelCase					: Union[str, Any]		    = ["Summary of the text.", "Another summary."]
                _lowerCamelCase					: Union[str, Any]		    = [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

                _lowerCamelCase					: Optional[Any]		    = tokenizer(__A,padding=__A  )
                _lowerCamelCase					: Optional[int]		    = [[0] * len(__A  ) for x in encoded_output["input_ids"]]
                _lowerCamelCase					: Any		    = tokenizer.pad(__A  )
                self.assertSequenceEqual(outputs["global_attention_mask"],__A  )



    def 	lowerCamelCase_	(						self   :							Any  ):
          pass



    def 	lowerCamelCase_	(						self   :							str  ):
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'  ):
                      _lowerCamelCase					: Optional[Any]		    = self.rust_tokenizer_class.from_pretrained(__A,**__A  )
                      _lowerCamelCase					: Dict		    = self.tokenizer_class.from_pretrained(__A,**__A  )
                      _lowerCamelCase					: Union[str, Any]		    = "A, <mask> AllenNLP sentence."
                      _lowerCamelCase					: Dict		    = tokenizer_r.encode_plus(__A,add_special_tokens=__A,return_token_type_ids=__A  )
                      _lowerCamelCase					: int		    = tokenizer_p.encode_plus(__A,add_special_tokens=__A,return_token_type_ids=__A  )
                      self.assertEqual(sum(tokens_r["token_type_ids"]  ),sum(tokens_p["token_type_ids"]  )  )
                      self.assertEqual(
                          sum(tokens_r["attention_mask"]  ) / len(tokens_r["attention_mask"]  ),sum(tokens_p["attention_mask"]  ) / len(tokens_p["attention_mask"]  ),)

                      _lowerCamelCase					: Optional[Any]		    = tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]  )
                      _lowerCamelCase					: Dict		    = tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]  )
                      self.assertSequenceEqual(tokens_p["input_ids"],[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]  )
                      self.assertSequenceEqual(tokens_r["input_ids"],[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]  )

                      self.assertSequenceEqual(
                          __A,["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]  )
                      self.assertSequenceEqual(
                          __A,["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]  )

"""simple docstring"""



import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
 import torch

 from transformers.generation import DisjunctiveConstraint




@require_torch
class 		a_    (			unittest.TestCase						):







    def   _snake_case       (      self      :							Union[str, Any]  )		->Any:






        '''simple docstring'''






        _UpperCAmelCase        = [[1, 2, 4], [1, 2, 3, 4]]
        _UpperCAmelCase        = DisjunctiveConstraint(__UpperCamelCase  )
        self.assertTrue(isinstance(dc.token_ids  ,       __UpperCamelCase  )  )

        with self.assertRaises(__UpperCamelCase  ):
            DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]  )  )

        with self.assertRaises(__UpperCamelCase  ):
            DisjunctiveConstraint([torch.LongTensor([1, 2, 4]  ), torch.LongTensor([1, 2, 3, 4, 5]  )]  )







    def   _snake_case       (      self      :							Optional[int]  )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase        = [[1, 2], [1, 2, 3, 4]]

        with self.assertRaises(__UpperCamelCase  ):
            DisjunctiveConstraint(__UpperCamelCase  )  # fails here







    def   _snake_case       (      self      :							Optional[int]  )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase        = [[1, 2, 3], [1, 2, 4]]

        _UpperCAmelCase        = DisjunctiveConstraint(__UpperCamelCase  )

        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = dc.update(1  )
        _UpperCAmelCase        = stepped is True and completed is False and reset is False
        self.assertTrue(__UpperCamelCase  )
        self.assertTrue(not dc.completed  )
        self.assertTrue(dc.current_seq == [1]  )

        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = dc.update(2  )
        _UpperCAmelCase        = stepped is True and completed is False and reset is False
        self.assertTrue(__UpperCamelCase  )
        self.assertTrue(not dc.completed  )
        self.assertTrue(dc.current_seq == [1, 2]  )

        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = dc.update(3  )
        _UpperCAmelCase        = stepped is True and completed is True and reset is False
        self.assertTrue(__UpperCamelCase  )
        self.assertTrue(dc.completed  )  # Completed!
        self.assertTrue(dc.current_seq == [1, 2, 3]  )







    def   _snake_case       (      self      :							List[Any]  )		->Any:






        '''simple docstring'''






        _UpperCAmelCase        = [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

        _UpperCAmelCase        = DisjunctiveConstraint(__UpperCamelCase  )

        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = dc.update(1  )
        self.assertTrue(not dc.completed  )
        self.assertTrue(dc.current_seq == [1]  )

        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = dc.update(2  )
        self.assertTrue(not dc.completed  )
        self.assertTrue(dc.current_seq == [1, 2]  )

        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = dc.update(4  )
        self.assertTrue(not dc.completed  )
        self.assertTrue(dc.current_seq == [1, 2, 4]  )

        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = dc.update(5  )
        self.assertTrue(dc.completed  )  # Completed!
        self.assertTrue(dc.current_seq == [1, 2, 4, 5]  )

        dc.reset()

        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = dc.update(1  )
        self.assertTrue(not dc.completed  )
        self.assertTrue(dc.remaining() == 3  )
        self.assertTrue(dc.current_seq == [1]  )

        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = dc.update(2  )
        self.assertTrue(not dc.completed  )
        self.assertTrue(dc.remaining() == 2  )
        self.assertTrue(dc.current_seq == [1, 2]  )

        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = dc.update(5  )
        self.assertTrue(dc.completed  )  # Completed!
        self.assertTrue(dc.remaining() == 0  )
        self.assertTrue(dc.current_seq == [1, 2, 5]  )